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08_Crossover.Rmd
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08_Crossover.Rmd
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---
title: "Crossover"
output:
workflowr::wflow_html:
code_folding: hide
df_print: paged
---
```{r knitr, include = FALSE}
DOCNAME = "08_Crossover"
knitr::opts_chunk$set(autodep = TRUE,
cache = FALSE,
cache.path = paste0("cache/", DOCNAME, "/"),
cache.comments = FALSE,
echo = TRUE,
error = FALSE,
fig.align = "center",
fig.width = 10,
fig.height = 8,
message = FALSE,
warning = FALSE)
```
```{r libaries, cache = FALSE}
# Presentation
library("knitr")
library("glue")
# Paths
library("here")
# Tidyverse
library("tidyverse")
```
```{r source, cache = FALSE}
source(here("R/output.R"))
```
```{r paths}
orgs.path <- here("output/04_Organoids_Clustering/cluster_assignments.csv")
orgs.neph.path <- here("output/04B_Organoids_Nephron/cluster_assignments.csv")
comb.path <- here("output/07_Combined_Clustering/cluster_assignments.csv")
comb.neph.path <- here("output/07B_Combined_Nephron/cluster_assignments.csv")
```
Introduction
============
In this document we are going to load the results of the various clustering
analyses and compare them. The goal is to see if they are consistent by checking
that clusters in different analyses that have been assigned the same cell types
actually contain the same cells.
```{r load, cache.extra = c(tools::md5sum(orgs.path), tools::md5sum(orgs.neph.path), tools::md5sum(comb.path), tools::md5sum(comb.neph.path))}
orgs.clusts <- read_csv(orgs.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(Organoids = Cluster)
orgs.neph.clusts <- read_csv(orgs.neph.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(OrgsNephron = Cluster)
comb.clusts <- read_csv(comb.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(Combined = Cluster)
comb.neph.clusts <- read_csv(comb.neph.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(CombNephron = Cluster)
clusts <- comb.clusts %>%
left_join(comb.neph.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode")) %>%
left_join(orgs.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode")) %>%
left_join(orgs.neph.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode"))
```
We are going to do this using a kind of heatmap. Clustering results from two
separate analyses will form the x and y axes and each cell will represent the
overlap in samples between two clusters. We will colour the cells using the
Jaccard index, a measure of similarity between to groups that is equal to the
size of the intersect divided by the size of the union. This will highlight
clusters that are particularly similar. We will also label cells with the
proportion of samples in a cluster that are also in another, so that rows and
columns will each sum to one (using a separate colour for each).
```{r summ-clusts}
summariseClusts <- function(clusts, clust1, clust2) {
clust1 <- enquo(clust1)
clust2 <- enquo(clust2)
clusts %>%
filter(!is.na(!!clust1) & !is.na(!!clust2)) %>%
group_by(!!clust1, !!clust2) %>%
summarise(Count = n()) %>%
ungroup() %>%
complete(!!clust1, !!clust2) %>%
mutate(!!quo_name(clust1) := factor(!!clust1),
!!quo_name(clust2) := factor(!!clust2)) %>%
group_by(!!clust1) %>%
mutate(!!paste0(quo_name(clust1), "Total") := sum(Count, na.rm = TRUE),
!!paste0(quo_name(clust1), "Pct") :=
Count / sum(Count, na.rm = TRUE)) %>%
group_by(!!clust2) %>%
mutate(!!paste0(quo_name(clust2), "Total") := sum(Count, na.rm = TRUE),
!!paste0(quo_name(clust2), "Pct") :=
Count / sum(Count, na.rm = TRUE)) %>%
mutate(Jaccard = Count /
(!!rlang::sym(paste0(quo_name(clust1), "Total")) +
!!rlang::sym(paste0(quo_name(clust2), "Total")) -
Count))
}
```
Organoids
=========
vs Organoids Nephron
--------------------
```{r orgs-orgs-neph}
summariseClusts(clusts, Organoids, OrgsNephron) %>%
ggplot(aes(x = Organoids, y = OrgsNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(OrgsNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({OrganoidsTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({OrgsNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Organoids cluster",
y = "Organoids nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
```
vs Combined
-----------
```{r orgs-combined}
summariseClusts(clusts, Organoids, Combined) %>%
ggplot(aes(x = Organoids, y = Combined, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 4) +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 4) +
geom_text(aes(label = glue("({OrganoidsTotal})")), y = -0.05,
size = 3.5, colour = "#ff698f") +
geom_text(aes(label = glue("({CombinedTotal})")), x = -0.05,
size = 3.5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Organoids cluster",
y = "Combined cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
```
vs Combined Nephron
-------------------
```{r orgs-comb-neph}
summariseClusts(clusts, Organoids, CombNephron) %>%
ggplot(aes(x = Organoids, y = CombNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(CombNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({OrganoidsTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({CombNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Organoids cluster",
y = "Combined nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
```
Combined
========
vs Combined Nephron
-------------------
```{r combined-comb-neph}
summariseClusts(clusts, Combined, CombNephron) %>%
ggplot(aes(x = Combined, y = CombNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(CombNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({CombinedTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({CombNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined cluster",
y = "Combined nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
```
vs Organoids
------------
```{r combined-orgs}
summariseClusts(clusts, Combined, Organoids) %>%
ggplot(aes(x = Combined, y = Organoids, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 4) +
geom_text(aes(label = round(OrganoidsPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 4) +
geom_text(aes(label = glue("({CombinedTotal})")), y = -0.05,
size = 4, colour = "#ff698f") +
geom_text(aes(label = glue("({OrganoidsTotal})")), x = -0.05,
size = 4, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined cluster",
y = "Organoids cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
```
vs Organoids Nephron
--------------------
```{r combined-orgs-neph}
summariseClusts(clusts, Combined, OrgsNephron) %>%
ggplot(aes(x = Combined, y = OrgsNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombinedPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(OrgsNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({CombinedTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({OrgsNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined cluster",
y = "Organoids nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
```
Combined nephron
================
vs Organoids Nephron
--------------------
```{r comb-neph-orgs-neph}
summariseClusts(clusts, CombNephron, OrgsNephron) %>%
ggplot(aes(x = CombNephron, y = OrgsNephron, fill = Jaccard)) +
geom_tile() +
geom_text(aes(label = round(CombNephronPct, 2)), nudge_y = 0.2,
colour = "#ff698f", size = 6) +
geom_text(aes(label = round(OrgsNephronPct, 2)), nudge_y = -0.2,
colour = "#73b4ff", size = 6) +
geom_text(aes(label = glue("({CombNephronTotal})")), y = -0.05,
size = 5, colour = "#ff698f") +
geom_text(aes(label = glue("({OrgsNephronTotal})")), x = -0.05,
size = 5, colour = "#73b4ff") +
scale_fill_viridis_c(begin = 0.02, end = 0.98, na.value = "black",
limits = c(0, 1)) +
coord_equal() +
expand_limits(x = -0.5, y = -0.5) +
labs(x = "Combined nephron cluster",
y = "Organoids nephron cluster",
caption = "Numbers in brackets show cluster size") +
theme_minimal() +
theme(axis.text = element_text(size = 20),
axis.text.x = element_text(colour = "#ff698f"),
axis.text.y = element_text(colour = "#73b4ff"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
axis.title.x = element_text(colour = "#ff698f"),
axis.title.y = element_text(colour = "#73b4ff"),
legend.key.height = unit(50, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
```
Summary
=======
Output files
------------
This table describes the output files produced by this document. Right click
and _Save Link As..._ to download the results.
```{r output}
dir.create(here("output", DOCNAME), showWarnings = FALSE)
write_csv(clusts, here("output", DOCNAME, "cluster_assignments.csv"))
kable(data.frame(
File = c(
glue("[cluster_assignments.csv]",
"({getDownloadURL('cluster_assignments.csv', DOCNAME)})")
),
Description = c(
"Cluster assignments for all clustering analyses"
)
))
```